Generative Engine Optimization

GEO for SEOs — explained by someone who’s spent years pushing AI to find out how it actually works

I’m Kent Lundin, a digital marketing professor at BYU-Idaho. I use a research method I call the AI Knowledge Leverage Method to understand how generative engines retrieve, rank, and cite content — and I teach what I find to SEO professionals who are serious about adapting.

Kent Lundin · Professor of Digital Marketing, BYU-Idaho · Researcher, AI-driven search visibility

Most GEO content online is thin. Here’s why.

Since ChatGPT went mainstream, everyone has an opinion on Generative Engine Optimization. Most of it recycles the same surface advice: add schema, write clearly, use headings. That’s not wrong — but it’s not enough, and it doesn’t come from anyone who has actually interrogated how these systems behave.

The honest problem is that GEO is genuinely hard to research. You can’t just run an experiment and measure rankings. The systems are opaque. The rules aren’t published. And most practitioners don’t have the digital marketing depth to know which questions are worth asking in the first place.

That’s the gap this site is built to fill. Not GEO as buzzword. GEO as a discipline — researched systematically, explained plainly, and built for SEO professionals who already know how search works and want to understand what’s changing.

My research methodology

The AI Knowledge Leverage Method

The AI Knowledge Leverage Method is my approach to GEO research: using deep digital marketing expertise to interrogate AI systems in ways that surface insights a generalist prompt can’t reach. Because I understand how search, content, entities, and structured data work at a foundational level, I know which questions expose how generative engines actually behave — not just what they say when you ask them nicely. The result is GEO understanding grounded in systematic inquiry, not speculation.

My focus and perspective

Why this site is different from other GEO resources

Most GEO content is written by people who understand AI but not search, or understand search but are just getting familiar with AI. That combination produces advice that sounds plausible but doesn’t hold up when you apply it.

This site sits at the intersection of both. Years of teaching and practicing digital marketing — SEO, content strategy, structured data, analytics — give me the domain knowledge to push AI systems past their generic answers and into the specific, testable mechanics of how generative retrieval actually works.

When I write about why entity relationships matter for GEO, it’s because I’ve spent time pressing AI systems to explain their own retrieval logic, cross-referencing those explanations with observed behavior, and filtering the results through a career’s worth of search experience. That’s a different kind of source than a blog post summarizing what ChatGPT said about itself.

What you’ll find here

This site is organized around the core concepts that determine GEO visibility — restructured around how the AI Knowledge Leverage Method approaches them, not around how they’re typically explained elsewhere.

Foundation

Entities and AI Understanding

How AI systems identify, classify, and connect entities — and why your site’s entity clarity determines whether you’re cited or ignored.

→ Entities in GEO

Structure

Schema and Structured Data

The machine-readable signals that tell AI systems what your content is, who created it, and how it relates to everything else.

→ Schema in GEO

Content

Answer-Optimized Writing

How to write content that AI systems can extract, trust, and cite — including the role of multimodal formats in improving interpretability.

→ Answer-Optimized Content

Retrieval

Retrieval and Ranking Optimization

What happens before the AI generates an answer — which sources get retrieved, which passages get prioritized, and which voices get trusted.

→ RRO in GEO

Authority

Authority and Trust Signals

How AI systems evaluate credibility — and what you can do to make your site a source they’re confident citing.

→ Authority in GEO

Measurement

Measuring GEO Visibility

Traditional SEO metrics don’t capture AI visibility. Here’s what to track instead and how to know if your GEO work is actually moving the needle.

→ GEO Measurement

Who This Site Is For

This site is written for SEO professionals who already have a working understanding of search and want to get ahead of what AI is changing — not for beginners looking for a GEO overview.

SEO professionals

You understand rankings, structured data, and content strategy. You want to know exactly what GEO changes about your existing work — and what it doesn’t.

Digital marketing practitioners

You’re responsible for organic visibility and AI is already reshaping how your audience finds information. You need a framework, not just tactics.

Agency professionals

Your clients are asking about AI search. You want answers grounded in research, not speculation — so you can advise with confidence.

Marketing educators

You’re teaching search and content strategy and need GEO explained in a way that’s accurate, structured, and actually teachable.

If you’re looking for quick wins or surface-level tactics, this will feel slow. The AI Knowledge Leverage Method surfaces deeper patterns than most GEO content — which means it takes longer to explain and requires more from the reader. That’s a feature, not a bug.

Common questions

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization is the discipline of making your content understandable, retrievable, and citable by AI systems like ChatGPT, Perplexity, Google Gemini, and Bing Copilot. Unlike traditional SEO, which focuses on ranking pages for keywords, GEO focuses on whether AI systems understand who you are, what you know, and when to reference you in generated answers.

How is GEO different from SEO?

SEO is primarily concerned with ranking in search results — getting pages to appear when users search for keywords. GEO is concerned with a different stage: whether AI systems retrieve your content as a source, extract accurate information from it, and include or cite it in generated responses. The underlying signals overlap — entity clarity, structured data, content quality — but the optimization goals and measurement approaches are distinct.

What is the AI Knowledge Leverage Method?

The AI Knowledge Leverage Method is the research approach Kent Lundin uses to study GEO. It involves using deep digital marketing expertise to interrogate AI systems in ways that surface how generative retrieval actually works — going beyond surface-level explanations by asking questions that only someone with years of search and content strategy experience would know to ask. The method produces GEO insights grounded in systematic inquiry rather than speculation or secondhand reporting.

If I already know SEO well, how long will it take to understand GEO?

If you have a solid SEO foundation — entities, structured data, content strategy, link authority — GEO builds directly on that knowledge. Most of the concepts translate; the framing and optimization targets shift. An experienced SEO professional who works through the core GEO concepts on this site systematically should reach a working understanding within a few focused sessions.

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Start with the foundation

The best place to begin is understanding how AI systems identify entities — because entity clarity underlies every other GEO concept. From there, the rest of the framework follows logically.

→ Start with Entities in GEO
Or see the full GEO framework →

Kent Lundin BYU-Idaho

Professor of Digital Marketing at BYU-Idaho. Researcher and practitioner focused on how AI systems are changing search visibility, content strategy, and digital marketing. Founder of kentlundin.com — where I document what the AI Knowledge Leverage Method surfaces about Generative Engine Optimization.